Sequences of purchases in credit card data reveal lifestyles in urban populations

Riccardo Di Clemente, Miguel Luengo-Oroz, Matias Travizano, Sharon Xu, Bapu Vaitla and Marta C. González ()
Additional contact information
Miguel Luengo-Oroz: United Nations Global Pulse
Matias Travizano: GranData
Sharon Xu: Massachusetts Institute of Technology
Bapu Vaitla: Harvard University
Marta C. González: Massachusetts Institute of Technology

Nature Communications, 2018, vol. 9, issue 1, 1-8

Abstract: Abstract Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.nature.com/articles/s41467-018-05690-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05690-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-05690-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().